On the Databases of Protein Biomarkers of Human Exposure to Environmental Hazards: A Literature Review

Introduction: Both environmental and occupational exposure to hazardous chemicals is a public health challenge since it can induce oxidative stress, lipid peroxidation, and protein modifications. Creation of a database linking work-related risk factors and adverse human health outcomes based on protein biomarkers will contribute to the development of effective preventive and therapeutic approaches.

Objective: To conduct a systematic review of literature to identify databases containing information about human protein biomarkers of exposure to environmental risk factors.

Materials and methods: We examined Russian and English-language publications containing information about existing protein biomarker databases issued in 2003–2023 and found in Elibary.ru, PubMed, Google Scholar, Scopus, and Research Gate using the following keywords: database, adverse effect, biomarkers, proteins, and occupational environment. Forty of 300 papers initially selected contained systematized data of research aimed at identifying biomarkers of occupational exposures and were therefore chosen for the review.

Results: Eight information sources have been found to match our search criteria. We have established that the databases are divided into two types by availability and accessibility of the information of interest. Type I databases contain research findings related to the impact of occupational exposures on the content of biomarkers (proteins) but have limited access. Type II databases are open to access, but they do not contain direct information about protein biomarkers associated with occupational exposures.

Conclusion: The existing databases either contain unsystematized data on protein biomarkers of adverse human health effects or are closed to access. Thus, the task of creating such publicly available information sources deems relevant.

1. Masyagutova LM, Abdrakhmanova ER, Gabdulvaleeva EF, Perminova VA. Risk of occupational, work-related, and somatic morbidity among metallurgical industries workers. Vestnik Avitsenny. 2021;23(2):280-290. doi: 10.25005/2074-0581-2021-23-2-280-290

2. Zaitseva NV, May IV, Shur PZ, Alekseev VB, Lebedeva-Nesevrya NA. [Practice of health risk assessment and management based on new methods and approaches.] In: [Health Risk Analysis in the Strategy of State Socio-Economic Development.] Perm: Perm National Research Polytechnic University; 2014: 625-711. (In Russ.)

3. Schulte PA, Hauser JE. The use of biomarkers in occupational health research, practice, and policy. Toxicol Lett. 2012;213(1):91-99. doi: 10.1016/j.toxlet.2011.03.027

4. Zheglova AV. Personified occupational risk for employees of mining enterprises. In: Bukhtiyarov IV, Rybina TM, eds. Health and Safety at the Workplace: Proceedings of the III International Scientific Forum, Novopolotsk–Polotsk, Belarus, May 15–17, 2019. Novopolotsk–Polotsk, Belarus: Polycraft LLC; 2019;1(3):113-117. (In Russ.) doi: 10.31089/978-985-7153-76-3-2019-1-3-113-117

5. Zaitseva NV, Shur PZ, Kostarev VG, Klimenko AR, Puzikov YuG, Bolotova EI. Substantiation of a research programme for powder metallurgy risk factor assessment. Vestnik Permskogo Unversiteta. Series: Biology. 2010;(2):50-56. (In Russ.)

6. Azhimetova GN, Koigeldinova SS. Prediction of anthracosilicosis development terms at miners of coal industry. Meditsina i Ekologiya. 2010;(2(55)):46-49. (In Russ.)

7. Davis AP, Murphy CG, Rosenstein MC, Wiegers TC, Mattingly CJ. The Comparative Toxicogenomics Database facilitates identification and understanding of chemical– gene–disease associations: Arsenic as a case study. BMC Med Genomics. 2008;1:48. doi: 10.1186/1755-8794-1-48

8. Hanzlik RP, Koen YM, Theertham B, Dong Y, Fang J. The reactive metabolite target protein database (TPDB) – a web-accessible resource. BMC Bioinformatics. 2007;8:95. doi: 10.1186/1471-2105-8-95

9. Lamurias A, Jesus S, Neveu V, Salek RM, Couto FM. Information retrieval using machine learning for biomarker curation in the Exposome–Explorer. Front Res Metr Anal. 2021;6:689264. doi: 10.3389/frma.2021.689264

10. Kim S. Public chemical databases. In: Ranganathan S, Gribskov M, Nakai K, SchÖnbach C. Encyclopedia of Bioinformatics and Computational Biology. Academic Press; 2019;2:628-639. doi: 10.1016/B978-0-12-809633-8.20192-1

11. Pawar G, Madden JC, Ebbrell D, Firman JW, Cronin MTD. In silico toxicology data resources to support read–across and (Q)SAR. Front Pharmacol. 2019;10:561. doi: 10.3389/fphar.2019.00561

12. Zaitseva NV, Shur PZ, Klimenko AR, Ustinova OYu, Lebedeva-Nesevria NA, Kostarev VG. Hygienic evaluation of risk factors on powder metallurgy production. Meditsina Truda i Promyshlennaya Ekologiya. 2011;(11):16-20. (In Russ.)

13. Kytikova OYu, Gvozdenko TA, Antonyuk MV. Modern aspects of prevalence of chronic bronchopulmonary diseases. Byulleten’ Fiziologii i Patologii Dykhaniya. 2017;(64):94-100. (In Russ.) doi: 10.12737/article_5936346fdfc1f3.32482903

14. Davis AP, Grondin CJ, Johnson RJ, et al. Comparative Toxicogenomics Database (CTD): Update 2021. Nucleic Acids Res. 2021;49(D1):D1138-D1143. doi: 10.1093/nar/gkaa891

15. Mattingly CJ. Chemical databases for environmental health and clinical research. Toxicol Lett. 2009;186(1):62-65. doi: 10.1016/j.toxlet.2008.10.003

16. Cai Y, Rosen Vollmar AK, Johnson CH. Analyzing metabolomics data for environmental health and exposome research. Methods Mol Biol. 2020;2104:447-467. doi: 10.1007/978-1-0716-0239-3_22

17. Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative Toxicogenomics Database (CTD): Update 2023. Nucleic Acids Res. 2023;51(D1):D1257-D1262. doi: 10.1093/nar/gkac833

18. Mattingly CJ, Colby GT, Rosenstein MC, Forrest JN Jr, Boyer JL. Promoting comparative molecular studies in environmental health research: An overview of the Comparative Toxicogenomics Database (CTD). Pharmacogenomics J. 2004;4(1):5-8. doi: 10.1038/sj.tpj.6500225

19. Mattingly CJ, Rosenstein MC, Colby GT, Forrest JN Jr, Boyer JL. The Comparative Toxicogenomics Database (CTD): A resource for comparative toxicological studies. J Exp Zool A Comp Exp Biol. 2006;305(9):689-692. doi: 10.1002/jez.a.307

20. Wiegers TC, Davis AP, Cohen KB, Hirschman L, Mattingly CJ. Text mining and manual curation of chemical-gene-disease networks for the Comparative Toxicogenomics Database (CTD). BMC Bioinformatics. 2009;10:326. doi: 10.1186/1471-2105-10-326

21. Mattingly CJ, Colby GT, Forrest JN, Boyer JL. The Comparative Toxicogenomics Database (CTD). Environ Health Perspect. 2003;111(6):793-795. doi: 10.1289/ehp.6028

22. Hanzlik RP, Koen YM, Garrett MJ. The reactive metabolite target protein database archive. Medicinal Chemistry Scholarly Works. 2020. Accessed February 21, 2024. https://kuscholarworks.ku.edu/handle/1808/30592

23. Hanzlik RP, Fang J, Koen YM. Filling and mining the reactive metabolite target protein database. Chem Biol Interact. 2009;179(1):38-44. doi: 10.1016/j.cbi.2008.08.016

24. Zhao F, Li L, Chen Y, et al. Risk-based chemical ranking and generating a prioritized human exposome database. Environ Health Perspect. 2021;129(4):47014. doi: 10.1289/ EHP7722

25. International Agency for Research on Cancer/World Health Organization. What is Exposome-Explorer? Accessed June 2, 2023. http://exposome-explorer.iarc.fr/about

26. Neveu V, Nicolas G, Salek RM, Wishart DS, Scalbert A. Exposome-Explorer 2.0: An update incorporating candidate dietary biomarkers and dietary associations with cancer risk. Nucleic Acids Res. 2020;48(D1):D908-D912. doi: 10.1093/nar/gkz1009

27. Neveu V, Moussy A, Rouaix H, et al. Exposome-Explorer: A manually-curated database on biomarkers of exposure to dietary and environmental factors. Nucleic Acids Res. 2017;45(D1):D979-D984. doi: 10.1093/nar/gkw980

28. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37(Web Server issue):W623-33. doi: 10.1093/nar/gkp456

29. Wang Y, Bolton E, Dracheva S, et al. An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2010;38(Database issue):D255-66. doi: 10.1093/nar/gkp965

30. Kim S, Chen J, Cheng T, et al. PubChem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373-D1380. doi: 10.1093/nar/gkac956

31. Li Q, Cheng T, Wang Y, Bryant SH. PubChem as a public resource for drug discovery. Drug Discov Today. 2010;15(23-24):1052-1057. doi: 10.1016/j.drudis.2010.10.003

32. National Library of Medicine. About PubChem. Accessed June 2, 2023. https://pubchem.ncbi.nlm.nih.gov/docs/about

33. Kim S, Thiessen PA, Bolton EE, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202–D1213. doi: 10.1093/nar/gkv951

34. Kim S, Chen J, Cheng T, et al. PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388-D1395. doi: 10.1093/nar/gkaa971

35. Musa A, Tripathi S, Dehmer M, Emmert-Streib F. L1000 Viewer: A search engine and web interface for the LINCS data repository. Front Genet. 2019;10:557. doi: 10.3389/fgene.2019.00557

36. Xie Z, Kropiwnicki E, Wojciechowicz ML, et al. Getting started with LINCS datasets and tools. Curr Protoc. 2022;2(7):e487. doi: 10.1002/cpz1.487

37. Cheng L, Li L. Systematic quality control analysis of LINCS data. CPT Pharmacometrics Syst Pharmacol. 2016;5(11):588-598. doi: 10.1002/psp4.12107

38. Stathias V, Turner J, Koleti A, et al. LINCS Data Portal 2.0: Next generation access point for perturbation–response signatures. Nucleic Acids Res. 2020;48(D1):D431-D439. doi: 10.1093/nar/gkz1023

39. Koleti A, Terryn R, Stathias V, et al. Data portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: Integrated access to diverse large-scale cellular perturbation response data. Nucleic Acids Res. 2018;46(D1):D558-D566. doi: 10.1093/nar/gkx1063

40. Vempati UD, Chung C, Mader C, et al. Metadata standard and data exchange specifications to describe, model, and integrate complex and diverse high-throughput screening data from the Library of Integrated Network-based Cellular Signatures (LINCS). J Biomol Screen. 2014;19(5):803-816. doi: 10.1177/1087057114522514